Due to the advent of novel technologies and digital opportunities allowing to simplify user lives, healthcare is increasingly evolving towards digitalization. This represent a great opportunity on one side but it also exposes healthcare organizations to multiple threats (both digital and not) that may lead an attacker to compromise the security of medial processes and potentially patients' safety. Today technical cybersecurity countermeasures are used to protect the confidentiality, integrity and availability of data and information systems - especially in the healthcare domain. This paper will report on the current state of the art about cyber security in the Healthcare domain with particular emphasis on current threats and methodologies to analyze and manage them. In addition, it will introduce a multi-layer attack model providing a new perspective for attack and threat identification and analysis.Passive health monitoring has been introduced as a solution for continuous diagnosis and tracking of subjects' condition with minimal effort. This is partially achieved by the technology of passive audio recording although it poses major audio privacy issues for subjects. Existing methods are limited to controlled recording environments and their prediction is significantly influenced by background noises. Meanwhile, they are too compute-intensive to be continuously running on smart phones. In this paper, we implement an efficient and robust audio privacy preserving method that profiles the background audio to focus only on audio activities detected during recording for performance improvement, and to adapt to the noise for more accurate speech segmentation. We analyze the performance of our method using audio data collected by a smart watch in lab noisy settings. Our obfuscation results show a low false positive rate of 20% with a 92% true positive rate by adapting to the recording noise level. We also reduced model memory footprint and execution time of the method on a smart phone by 75% and 62% to enable continuous speech obfuscation.Critical care patients experience varying levels of pain during their stay in the intensive care unit, often requiring administration of analgesics and sedation. Such medications generally exacerbate the already sedentary physical activity profiles of critical care patients, contributing to delayed recovery. Thus, it is important not only to minimize pain levels, but also to optimize analgesic strategies in order to maximize mobility and activity of ICU patients. Currently, we lack an understanding of the relation between pain and physical activity on a granular level. In this study, we examined the relationship between nurse assessed pain scores and physical activity as measured using a wearable accelerometer device. We found that average, standard deviation, and maximum physical activity counts are significantly higher before high pain reports compared to before low pain reports during both daytime and nighttime, while percentage of time spent immobile was not significantly different between the two pain report groups. Clusters detected among patients using extracted physical activity features were significant in adjusted logistic regression analysis for prediction of pain report group.Automatic cough detection using audio has advanced passive health monitoring on devices such as smart phones and wearables; it enables capturing longitudinal health data by eliminating user interaction and effort. One major issue arises when coughs from surrounding people are also detected; capturing false coughs leads to significant false alarms, excessive cough frequency, and thereby misdiagnosis of user condition. To address this limitation, in this paper, a method is proposed that creates a personal cough model of the primary subject using limited number of cough samples; the model is used by the automatic cough detection to verify whether the identified coughs match the personal pattern and belong to the primary subject. A Gaussian mixture model is trained using audio features from cough to implement the subject verification method; novel cough embeddings are learned using neural networks and integrated into the model to further improve the prediction accuracy. We analyze the performance of the method using our cough dataset collected by a smart phone in a clinical study. Population in the dataset involves subjects categorized of healthy or patients with COPD or Asthma, with the purpose of covering a wider range of pulmonary conditions. Cross-subject validation on a diverse dataset shows that the method achieves an average error rate of less than 10%, using a personal cough model generated by only 5 coughs from the primary subject.Despite the prevalence of respiratory diseases, their diagnosis by clinicians is challenging. Accurately assessing airway sounds requires extensive clinical training and equipment that may not be easily available. Current methods that automate this diagnosis are hindered by their use of features that require pulmonary function tests. We leverage the audio characteristics of coughs to create classifiers that can distinguish common respiratory diseases in adults. Moreover, we build on recent advances in generative adversarial networks to augment our dataset with cleverly engineered synthetic cough samples for each class of major respiratory disease, to balance and increase our dataset size. We experimented on cough samples collected with a smartphone from 45 subjects in a clinic. Our CoughGAN-improved Support Vector Machine and Random Forest models show up to 76% test accuracy and 83% F1 score in classifying subjects' conditions between healthy and three major respiratory diseases. Adding our synthetic coughs improves the performance we can obtain from a relatively small unbalanced healthcare dataset by boosting the accuracy over 30%. Our data augmentation reduces overfitting and discourages the prediction of a single, dominant class. These results highlight the feasibility of automatic, cough-based respiratory disease diagnosis using smartphones or wearables in the wild.This paper describes the effects of a smartphone-based wearable telerehabilitation system (called Smarter Balance System, SBS) intended for in-home dynamic weight-shifting balance exercises (WSBEs) by individuals with Parkinson's disease (PD). Two individuals with idiopathic PD performed in-home dynamic WSBEs in anterior-posterior (A/P) and medial-lateral (M/L) directions, using the SBS 3 days per week for 6 weeks. Exercise performance was quantified by cross-correlation (XCORR) and position error (PE) analyses. https://www.selleckchem.com/products/Vorinostat-saha.html Balance and gait performance and level of fear of falling were assessed by limit of stability (LOS), Sensory Organization Test (SOT), Falls Efficacy Scale (FES), Activities-specific Balance Confidence (ABC), and Dynamic Gait Index (DGI) at the pre-(beginning of week 1), post-(end of week 6), and retention-(1 month after week 6) assessments. Regression analyses found that exponential trends of the XCORR and PE described exercise performance more effectively than linear trends. Range of LOS in both A/P and M/L directions improved at the post-assessment compared to the pre-assessment, and was retained at the retention assessment.